Machine learning calculation model for hydrodynamic lubrication characteristics of a miter gate bottom pivot

DOI10.1016/J.ENGANABOUND.2022.05.024OpenAlexW4281805463WikidataQ113875210 ScholiaQ113875210MaRDI QIDQ2085842

Xiang Xu, Xinze Zhao, Grzegorz Królczyk, Zhengguo Guan, Maciej Sulowicz, Tiancan Dai, Zhi Xiong Li

Publication date: 19 October 2022

Published in: Engineering Analysis with Boundary Elements (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.enganabound.2022.05.024

zbMATH Keywords

genetic algorithmhydrodynamic lubricationmiter gatebottom pivot

Mathematics Subject Classification ID

Biology and other natural sciences (92-XX) Fluid mechanics (76-XX)

Cites Work

• Optimum design of rolling element bearings using genetic algorithms
• Multi-objective design optimisation of rolling bearings using genetic algorithms

This page was built for publication: Machine learning calculation model for hydrodynamic lubrication characteristics of a miter gate bottom pivot